{"id":615215,"date":"2026-09-23T11:42:35","date_gmt":"2026-09-23T11:42:35","guid":{"rendered":"https:\/\/www.olympiajournal.com\/news\/story\/615215\/jinba-launches-a-mckinsey-slide-deck-skill-that-turns-aigenerated-slides-into-boardroomready-presentations.html"},"modified":"2026-09-23T11:42:35","modified_gmt":"2026-09-23T11:42:35","slug":"jinba-launches-a-mckinsey-slide-deck-skill-that-turns-aigenerated-slides-into-boardroomready-presentations","status":"publish","type":"post","link":"https:\/\/www.olympiajournal.com\/news\/story\/615215\/jinba-launches-a-mckinsey-slide-deck-skill-that-turns-aigenerated-slides-into-boardroomready-presentations.html","title":{"rendered":"Jinba Launches a McKinsey Slide Deck Skill That Turns AI-Generated Slides Into Boardroom-Ready Presentations"},"content":{"rendered":"
Jinba, the YC-backed AI workflow platform for regulated enterprises, has launched a McKinsey slide deck skill inside Jinba App. Authored by a former McKinsey consultant, the skill turns generic AI-generated slides into boardroom-ready consulting slide decks using a ~110-item slide-design rulebook. The open-source version earned 461 GitHub stars and 41 forks.<\/div>\n

SAN FRANCISCO – September 23, 2026 –<\/strong> Jinba, the YC-backed AI workflow platform for regulated enterprises, has introduced a McKinsey slide deck skill inside Jinba App — a capability that turns generic, obviously-AI-generated slides into a consulting slide deck an executive team can present to the board.<\/p>\n

The skill is authored by a former McKinsey consultant who went on to co-found the company, and it encodes roughly 110 slide-design rules collected from real consulting review feedback into a rulebook the AI reads before producing every deck. That is the distinction that makes the output a genuine consulting slide deck rather than merely a polished one: the open-source version of the skill attracted 461 GitHub stars and 41 forks<\/strong>, and an accompanying writeup on the company’s official Note account drew more than 260 likes.<\/p>\n

Most AI presentation tools produce decks that look clean but collapse on closer reading — titles that open with category labels like “Current State and Challenges,” three-column layouts that offload the thinking onto the reader, and a conclusion repeated three times across title, body, and a bottom banner. Jinba’s thesis is that the difference between a generic AI deck and a consulting slide deck is not a nicer template — it is a set of review rules that survive from one AI session to the next.<\/p>\n

The skill ships as part of Jinba App rather than as a standalone tool, so teams generate decks inside the same governed, permission-controlled environment they already use for workflow execution. The open-source rulebook and parts library are available on GitHub for the developer community, while the managed, team-governed version runs inside Jinba App.<\/p>\n

Key Capabilities<\/p>\n

– A 110-item slide-design rulebook<\/strong> — the “source of truth” file encoding assertion-first titles, premise-to-conclusion layouts, and consistent terminology, accumulated from real review feedback.- A 62-type slide catalog<\/strong> — a visual reference of proven consulting layouts, from issue trees to delta bridges to executive summaries, used as an idea book rather than a rigid template.- Machine rule checking<\/strong> — an automated checker (`check_deck.py`) that flags rule violations, leftover placeholders, and structural defects before a human ever sees the deck.- Independent review<\/strong> — a built-in practice of having a separate AI agent that did not build the deck read it back for logic, contradictions, and language, mirroring a real consulting review pass.<\/p>\n

About the Approach<\/strong><\/p>\n

The skill’s public release is part of a broader pattern for how Jinba builds capabilities: encode professional judgment into a structured, persistent format that AI reads each time, rather than relying on ad-hoc prompting. This mirrors the company’s “Company Brain” direction, where enterprise knowledge is modeled into a bespoke schema instead of retrieved by surface similarity — the same reason generative AI pilots fail in banking and insurance<\/a> when output looks polished but isn’t grounded in the right context.<\/p>\n

The technique has already been validated against leading models. The company published side-by-side comparisons of Claude Opus 5 and GPT-6 Astra on identical deck-building tasks, showing the skill upgrading output quality on both — an extension of the same AI governance discipline Jinba <\/a><\/p>\n

About Jinba<\/strong><\/p>\n

Jinba is a San Francisco-headquartered, YC-backed (W26) AI workflow platform that lets business users in highly regulated industries — banking, insurance, legal, healthcare, and pharma — build production-ready AI workflows with natural language prompts that compile directly into APIs and MCP servers. Its SOC II–compliant platform ships governed automations in days rather than months, on-premise or air-gapped, with deterministic execution, audit logging, RBAC, and SSO. For more information, visit jinba.io<\/a>.<\/p>\n

Media Contact: <\/strong>Jinba Media Relations contact@jinba.io<\/p>\n

Media Contact<\/span>
Company Name:<\/strong> Jinba<\/a>
Contact Person:<\/strong> Media Relations
Email:<\/strong> Send Email<\/a>
Address:<\/strong>1301 16th St
City:<\/strong> San Francisco
State:<\/strong> CA
Country:<\/strong> United States
Website:<\/strong> https:\/\/jinba.io<\/a><\/p>\n

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